Scott Vossen
Senior Software Engineer at Microsoft
Eden Prairie, Minnesota, United States
Summary
🤩
Rockstar🎓
Top SchoolScott Vossen is a Senior Software Engineer at Microsoft who leads the Nonprofit Offers team and contributes to Sales Agent and Copilot initiatives, owning the Microsoft Cloud for Nonprofits. He designs and ships cloud-native, greenfield microservice architectures with CI/CD and IaC (Bicep), and built automation for integration, load, localization and disaster recovery scenarios. An active open-source contributor to Microsoft LightGBM and SynapseML, he implemented streaming APIs that cut client-side memory use and improved distributed dataset handling in a widely used gradient boosting library. His background as a principal engineer and solution architect spans analytics, ML-backed products, and high-scale donor platforms, blending backend ML engineering with platform delivery. Early training in architectural drafting gives him a practical, design-oriented approach to system architecture and tooling.
3 years of coding experience
8 years of employment as a software developer
Bachelor of Science (B.S.), Software Engineering, Bachelor of Science (B.S.), Software Engineering at Minnesota State University, MankatoAssociates of Applied Science, Architectural Drafting and Architectural CAD/CADD, Associates of Applied Science, Architectural Drafting and Architectural CAD/CADD at South Central College
English, German
Stackoverflow
37reputation
2kreached
1answer
5questions
Github Skills (29)
apache-spark10
c-language10
data-science10
data-mining10
big-data10
machine-learning10
distributed-systems10
scala210
gradient-boosting10
ml10
mle10
scala10
distributed-system10
lightgbm10
c-programming-language10
Programming languages (2)
Github contributions (5)
microsoft/SynapseML
Mar 2022 - Jan 2023
Simple and Distributed Machine Learning
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:98 reviews, 23 commits, 50 PRs in 9 months
Contributions summary:Scott primarily focused on improving the LightGBM integration within the SynapseML project. Their contributions include fixing issues related to class loading in IntelliJ, refactoring parameter systems, fixing model saving, and addressing multiclass training with initial scores. The code changes reveal involvement in the LightGBM booster, dataset aggregation, and parameter handling, indicating significant work in the backend machine learning aspects of the project.
fairness-mlpythoncaffe2model-deploymentcognitive-services
microsoft/LightGBM
Jul 2022 - Nov 2022
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:29 reviews, 6 commits, 9 PRs in 4 months
Contributions summary:Scott primarily focused on enhancing the LightGBM library's core functionality, specifically improving its streaming APIs to reduce client-side memory usage and handle distributed data more effectively. Their work involved modifying the C API, dataset loading, and internal data structures to support true streaming and concurrency. They implemented and tested features for streaming datasets, including dense and sparse data formats, which improved performance and efficiency. These changes are instrumental in handling larger datasets and optimizing the machine learning framework's performance.
kagglepythondata-mininglightgbmmicrosoft
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